Simulating Faults for Efficient Fault Recognition Training: An Implementation of Educational Automotive Board

Authors

  • Elroy FKP Tarigan
  • Mada Jimmy Politeknik Astra

DOI:

https://doi.org/10.26905/jeemecs.v8i2.15709

Keywords:

Automotive Training, Fault Simulator, Microcontroller, Engine Electrical

Abstract

As vehicle technology advances and complexity increases, effective automotive education requires practical training methods to develop competent human resources. This research addresses this need by transforming a standard electronically fuel-injected vehicle into reliable engine electrical laboratory equipment, utilizing an STM32 microcontroller and FreeRTOS for rapid data processing and precise control within a fault simulator. This system enhances automotive training by minimizing component damage and expediting fault scenario cycling through a personal computer-managed digital selector that controls 14 switches connected to critical engine sensors and actuators via a 50-pin ECU pinout for targeted fault injection and diagnostics. Ultimately, this specialized tool significantly improves operational efficiency, reduces diagnostic preparation time, minimizes component damage, and quantitatively enhances quality metrics, as evidenced by a reduction in Defects Per Million Opportunities (DPMO) from 208.33 to 69.44.

Downloads

Download data is not yet available.

References

[1] W. Hannibal, M. Fiolka, and R. Otto, “Innovative Training Methods in Automotive Engineering Using Virtual Reality,” ATZ worldwide, vol. 126, no. 5, pp. 36–41, 2024, doi: 10.1007/s38311-024-1909-4.

[2] P. Goyal, S. Fegade, D. Dabhilkar, and S. Petkar, “Fault simulator of car engine,” International Journal of Scientific and Research Publications (IJSRP), vol. 9, no. 1, p. p8589, Jan. 2019, doi: 10.29322/ijsrp.9.01.2019.p8589.

[3] R. et al Fischer, Modern Automotive Technology, 2nd ed. Haan-Gruiten, 2014.

[4] FreeRTOS, “RTOS Fundamentals.” Accessed: Jan. 22, 2025. [Online]. Available: https://www.freertos.org/

[5] STMicroelectronics, “STM32 32-bit Arm Cortex MCUs,” www.st.com. Accessed: Jan. 22, 2025. [Online]. Available: https://www.st.com/content/st_com/en.html

[6] W. Wu, “Automotive Motor Fault Diagnosis Model Integrating Machine Learning Algorithm and Fuzzy Control Theory,” International Journal of Fuzzy Systems, 2025, doi: 10.1007/s40815-024-01926-6.

[7] S. Papamatthaiou, P. Menelaou, B. El Achab Oussallam, and D. Moschou, “Recent advances in bio-microsystem integration and Lab-on-PCB technology,” Microsyst Nanoeng, vol. 11, no. 1, p. 78, 2025, doi: 10.1038/s41378-025-00940-4.

[8] K. Reif and K.-H. Dietsche, Automotive Handbook, 9th ed. Karlsruhe: Robert Bosch GmbH, 2014.

[9] Toyota Corporation, “Toyota Avanza Repair Manual,” 2013, Toyota Corporation.

[10] J. L. Rodríguez-Álvarez, J. L. García Alcaraz, C. Navarrete-Molina, and A. Soto-Cabral, “Root Cause Analysis (RCA),” in Lean Manufacturing in Latin America: Concepts, Methodologies and Applications, J. L. García Alcaraz, G. C. Robles, and A. Realyvásquez Vargas, Eds., Cham: Springer Nature Switzerland, 2025, pp. 439–468. doi: 10.1007/978-3-031-70984-5_19.

[11] B. and S. C. Niemann Jörg and Reich, “Lean Six Sigma,” in Lean Six Sigma: Methods for Production Optimization, Berlin, Heidelberg: Springer Berlin Heidelberg, 2024, pp. 9–59. doi: 10.1007/978-3-662-68744-4_3.

Downloads

Published

2025-08-26

Issue

Section

Articles